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57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
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from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import HumanevalXDataset, HumanevalXEvaluator
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humanevalx_reader_cfg = dict(
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input_columns=['prompt'], output_column='task_id', train_split='test')
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# This prompt is used for WizardLMCode series
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# You can use 620cfa for basic generation
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humanevalx_infer_cfg = {
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lang: dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt=
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f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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Create a {lang} script for this problem:
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{{prompt}}
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### Response:"""),
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])),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=1024))
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for lang in ['python', 'cpp', 'go', 'java', 'js']
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}
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humanevalx_eval_cfg_dict = {
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lang: dict(
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evaluator=dict(
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type=HumanevalXEvaluator,
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language=lang,
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ip_address=
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"localhost", # replace to your code_eval_server ip_address, port
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port=5000
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), # refer to https://github.com/Ezra-Yu/code-evaluator to launch a server
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pred_role='BOT')
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for lang in ['python', 'cpp', 'go', 'java', 'js'
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] # do not support rust now
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}
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humanevalx_datasets = [
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dict(
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type=HumanevalXDataset,
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abbr=f'humanevalx-{lang}',
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language=lang,
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path='./backup_data/humanevalx',
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reader_cfg=humanevalx_reader_cfg,
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infer_cfg=humanevalx_infer_cfg[lang],
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eval_cfg=humanevalx_eval_cfg_dict[lang])
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for lang in ['python', 'cpp', 'go', 'java', 'js']
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]
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